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題名 類別資料探索 - 影響NBA球員分數的變數選取
Categorical Exploratory Data Analysis - Feature Selection for Average Scores of NBA Players作者 趙立騰
Chao, Li-Teng貢獻者 周珮婷<br>張育瑋
Chou, Pei-Ting<br>Chang, Yu-Wei
趙立騰
Chao, Li-Teng關鍵詞 NBA
條件熵
互信息
特徵選取
類別資料分析
NBA
Conditional Entropy
Mutual Information
Feature Selection
Categorical Data Analysis日期 2023 上傳時間 6-Jul-2023 17:05:27 (UTC+8) 摘要 條件熵是信息理論中的一個重要概念，用於量化給定一個隨機變數的值的條件下，另一個變量的不確定性。本論文利用條件熵以及條件熵下降的概念對 NBA 球員資料做類別資料分析，試著找出影響平均得分最為重要的變數，透過結合變數從條件熵獲得更多的訊息再加以分析，找出的關鍵變數為球權使用率及籃板，並針對 11 位現今 NBA的知名球員、特定球員 Dwight Howard 及 Carmelo Anthony 做分析，找出影響知名球員的變數為球員本身，Dwight Howard 最關鍵的變數為真實命中率、籃板及年齡，Carmelo Anthony 則是真實命中率，最後再將結果與隨機森林方法的重要變數比較。
Conditional entropy is a crucial concept in information theory, utilized to measure the uncertainty of one variable given the value of another random variable. This study applies the concept of conditional entropy and examines conditional entropy drops to conduct a categorical data analysis on NBA player data, aiming to identify the most influential variables impacting average scores. By incorporating additional variables to extract more information from conditional entropy, we deepen our analysis. The key variables identified include usg_pct and reb. Our analysis focuses on eleven prominent contemporary NBA players, with specific attention given to Dwight Howard and Carmelo Anthony. The variable found to influence prominent players is player_name. For Dwight Howard, the critical variables found to influence his performance are ts_pct, reb, and age. Meanwhile, for Carmelo Anthony,the defining variable is ts_pct. Finally, we compare our results with the important variables determined by the Random Forest method.參考文獻 Breiman, L. (2001). Random forests. Machine learning, 45:5–32.Cao, C. (2012). Sports data mining technology used in basketball outcome prediction.Chen, T.-L., Chou, E. P., and Fushing, H. (2021). Categorical nature of major factor selection via information theoretic measurements. Entropy, 23(12):1684.Cirtautas, J. (2022). Nba players. https://www.kaggle.com/datasets/justinas nba-players-data.Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine learning, 20:273–297.Duda, R. O., Hart, P. E., et al. (1973). Pattern classification and scene analysis, volume 3.Wiley New York.Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of eugenics, 7(2):179–188.Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection.Journal of machine learning research, 3(Mar):1157–1182.Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine learning, 46:389–422.Hlaváčková-Schindler, K., Paluš, M., Vejmelka, M., and Bhattacharya, J. (2007). Causality detection based on information-theoretic approaches in time series analysis. Physics Reports, 441(1):1–46.Hu, Q., Yu, D., Liu, J., and Wu, C. (2008). Neighborhood rough set based heterogeneous feature subset selection. Information sciences, 178(18):3577–3594.Kira, K. and Rendell, L. A. (1992). A practical approach to feature selection. In Machine learning proceedings 1992, pages 249–256. Elsevier.Kononenko, I. et al. (1994). Estimating attributes: Analysis and extensions of relief. In ECML, volume 94, pages 171–182. Citeseer.LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. nature, 521(7553):436–444.Liaw, A. and Wiener, M. (2002). Classification and regression by randomforest. R News, 2(3):18–22.Loeffelholz, B., Bednar, E., and Bauer, K. W. (2009). Predicting nba games using neural networks. Journal of Quantitative Analysis in Sports, 5(1).Meyer, P. E. (2022). infotheo: Information-Theoretic Measures. R package version 1.2.0.1.Oughali, M. S., Bahloul, M., and El Rahman, S. A. (2019). Analysis of nba players and shot prediction using random forest and xgboost models. In 2019 InternationalConference on Computer and Information Sciences (ICCIS), pages 1–5. IEEE.Pawlak, Z. (1982). Rough sets. International journal of computer & information sciences, 11:341–356.Peng, H., Long, F., and Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on pattern analysis and machine intelligence, 27(8):1226–1238.Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net.Journal of the royal statistical society: series B (statistical methodology), 67(2):301–320. 描述 碩士
國立政治大學
統計學系
110354017資料來源 http://thesis.lib.nccu.edu.tw/record/#G0110354017 資料類型 thesis dc.contributor.advisor 周珮婷<br>張育瑋 zh_TW dc.contributor.advisor Chou, Pei-Ting<br>Chang, Yu-Wei en_US dc.contributor.author (Authors) 趙立騰 zh_TW dc.contributor.author (Authors) Chao, Li-Teng en_US dc.creator (作者) 趙立騰 zh_TW dc.creator (作者) Chao, Li-Teng en_US dc.date (日期) 2023 en_US dc.date.accessioned 6-Jul-2023 17:05:27 (UTC+8) - dc.date.available 6-Jul-2023 17:05:27 (UTC+8) - dc.date.issued (上傳時間) 6-Jul-2023 17:05:27 (UTC+8) - dc.identifier (Other Identifiers) G0110354017 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/145945 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 統計學系 zh_TW dc.description (描述) 110354017 zh_TW dc.description.abstract (摘要) 條件熵是信息理論中的一個重要概念，用於量化給定一個隨機變數的值的條件下，另一個變量的不確定性。本論文利用條件熵以及條件熵下降的概念對 NBA 球員資料做類別資料分析，試著找出影響平均得分最為重要的變數，透過結合變數從條件熵獲得更多的訊息再加以分析，找出的關鍵變數為球權使用率及籃板，並針對 11 位現今 NBA的知名球員、特定球員 Dwight Howard 及 Carmelo Anthony 做分析，找出影響知名球員的變數為球員本身，Dwight Howard 最關鍵的變數為真實命中率、籃板及年齡，Carmelo Anthony 則是真實命中率，最後再將結果與隨機森林方法的重要變數比較。 zh_TW dc.description.abstract (摘要) Conditional entropy is a crucial concept in information theory, utilized to measure the uncertainty of one variable given the value of another random variable. This study applies the concept of conditional entropy and examines conditional entropy drops to conduct a categorical data analysis on NBA player data, aiming to identify the most influential variables impacting average scores. By incorporating additional variables to extract more information from conditional entropy, we deepen our analysis. The key variables identified include usg_pct and reb. Our analysis focuses on eleven prominent contemporary NBA players, with specific attention given to Dwight Howard and Carmelo Anthony. The variable found to influence prominent players is player_name. For Dwight Howard, the critical variables found to influence his performance are ts_pct, reb, and age. Meanwhile, for Carmelo Anthony,the defining variable is ts_pct. Finally, we compare our results with the important variables determined by the Random Forest method. en_US dc.description.tableofcontents 摘要 iAbstract ii目次 iii圖目錄 v表目錄 vi第 一 章 緒論 11.1 特徵選取 2第 二 章 文獻回顧 42.1 特徵選取 42.2 NBA 資料集 5第 三 章 研究方法 63.1 條件熵 83.2 隨機森林 10第 四 章 資料介紹 114.1 探索性資料分析 134.2 資料類別化 20第 五 章 研究結果 215.1 所有球員 215.1.1 CEDA 方法 215.1.2 RF 方法 235.2 知名球員 245.2.1 CEDA 方法 245.2.2 RF 方法 255.3 特定球員 265.3.1 Dwight Howard 265.3.2 Carmelo Anthony 27第 六 章 結論與建議 296.1 研究結論 296.2 未來方向與建議 30參考文獻 31 zh_TW dc.format.extent 1048773 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0110354017 en_US dc.subject (關鍵詞) NBA zh_TW dc.subject (關鍵詞) 條件熵 zh_TW dc.subject (關鍵詞) 互信息 zh_TW dc.subject (關鍵詞) 特徵選取 zh_TW dc.subject (關鍵詞) 類別資料分析 zh_TW dc.subject (關鍵詞) NBA en_US dc.subject (關鍵詞) Conditional Entropy en_US dc.subject (關鍵詞) Mutual Information en_US dc.subject (關鍵詞) Feature Selection en_US dc.subject (關鍵詞) Categorical Data Analysis en_US dc.title (題名) 類別資料探索 - 影響NBA球員分數的變數選取 zh_TW dc.title (題名) Categorical Exploratory Data Analysis - Feature Selection for Average Scores of NBA Players en_US dc.type (資料類型) thesis en_US dc.relation.reference (參考文獻) Breiman, L. (2001). Random forests. Machine learning, 45:5–32.Cao, C. (2012). Sports data mining technology used in basketball outcome prediction.Chen, T.-L., Chou, E. P., and Fushing, H. (2021). Categorical nature of major factor selection via information theoretic measurements. Entropy, 23(12):1684.Cirtautas, J. (2022). Nba players. https://www.kaggle.com/datasets/justinas nba-players-data.Cortes, C. and Vapnik, V. (1995). Support-vector networks. Machine learning, 20:273–297.Duda, R. O., Hart, P. E., et al. (1973). Pattern classification and scene analysis, volume 3.Wiley New York.Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of eugenics, 7(2):179–188.Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection.Journal of machine learning research, 3(Mar):1157–1182.Guyon, I., Weston, J., Barnhill, S., and Vapnik, V. (2002). Gene selection for cancer classification using support vector machines. Machine learning, 46:389–422.Hlaváčková-Schindler, K., Paluš, M., Vejmelka, M., and Bhattacharya, J. (2007). Causality detection based on information-theoretic approaches in time series analysis. Physics Reports, 441(1):1–46.Hu, Q., Yu, D., Liu, J., and Wu, C. (2008). Neighborhood rough set based heterogeneous feature subset selection. Information sciences, 178(18):3577–3594.Kira, K. and Rendell, L. A. (1992). A practical approach to feature selection. In Machine learning proceedings 1992, pages 249–256. Elsevier.Kononenko, I. et al. (1994). Estimating attributes: Analysis and extensions of relief. In ECML, volume 94, pages 171–182. Citeseer.LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. nature, 521(7553):436–444.Liaw, A. and Wiener, M. (2002). Classification and regression by randomforest. R News, 2(3):18–22.Loeffelholz, B., Bednar, E., and Bauer, K. W. (2009). Predicting nba games using neural networks. Journal of Quantitative Analysis in Sports, 5(1).Meyer, P. E. (2022). infotheo: Information-Theoretic Measures. R package version 1.2.0.1.Oughali, M. S., Bahloul, M., and El Rahman, S. A. (2019). Analysis of nba players and shot prediction using random forest and xgboost models. In 2019 InternationalConference on Computer and Information Sciences (ICCIS), pages 1–5. IEEE.Pawlak, Z. (1982). Rough sets. International journal of computer & information sciences, 11:341–356.Peng, H., Long, F., and Ding, C. (2005). Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Transactions on pattern analysis and machine intelligence, 27(8):1226–1238.Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net.Journal of the royal statistical society: series B (statistical methodology), 67(2):301–320. zh_TW
